Advances in Stress-Strength Modeling with Complete and Censored Data

Stress-strength modeling is a fundamental concept in reliability theory and survival analysis, quantifying the probability that a system’s strength exceeds the applied stress. It is widely used in engineering, medical research, and risk assessment to evaluate component reliability and system performance. In practice, lifetime data may be fully observed or incomplete due to experimental constraints, resulting in complete or censored observations. Traditional statistical methods may be inadequate for handling complex censoring schemes such as progressive, hybrid, or adaptive censoring. Recent developments in statistical modeling, computational methods, and lifetime distributions have enabled more accurate estimation of stress-strength reliability under complete and censored data. These advances provide both theoretical insights and practical tools for reliability assessment in real-world applications.The goal of this Research Topic is to bring together cutting-edge research on stress-strength modeling in the presence of complete and censored data. While the stress-strength framework has been extensively studied, challenges remain in accurately estimating reliability when data are fully observed, partially observed, or subject to complex censoring schemes. This Research Topic seeks to address methodological, computational, and applied aspects of stress-strength analysis, including classical and Bayesian estimation, parametric and nonparametric approaches, and novel lifetime distributions. Authors are invited to propose innovative solutions for handling complete data as well as progressive, hybrid, or adaptive censoring schemes, improving the efficiency and robustness of estimation methods. In addition, real-life applications demonstrating the practical utility of these approaches in engineering, medicine, environmental studies, and quality control are highly encouraged. By consolidating recent advances, this Research Topic aims to advance both theory and practice, providing a comprehensive reference for researchers and practitioners working with complete and censored reliability data.This Research Topic covers all aspects of stress-strength modeling for complete and censored data, including but not limited to:- Statistical inference for stress-strength reliability under complete data and Type-I, Type-II, progressive, hybrid, or adaptive censoring.- Multicomponent stress-strength reliability models.- Development and application of new lifetime distributions.- Classical and Bayesian estimation methods.- Nonparametric and semiparametric approaches.- Simulation studies and computational algorithms (e.g., EM, bootstrap, MCMC).- Real-world applications in engineering, medical, environmental, and quality control contexts.We welcome original research articles, methodological studies, applied research, and comprehensive reviews. Manuscripts should contribute novel insights or techniques that advance the theory, methodology, or application of stress-strength modeling—including multicomponent systems—with complete, censored, or incomplete data. Topic editors: Reza Pakyari, Ayman Baklizi Source: https://www.frontiersin.org/research-topics/79434/advances-in-stress-strength-modeling-with-complete-and-censored-data